R Markdown
install.packages("arules")
##
## The downloaded binary packages are in
## /var/folders/l_/k6t9zw295sn46sr8s62l6cb80000gn/T//RtmpZ9d9ED/downloaded_packages
install.packages("arulesViz")
##
## The downloaded binary packages are in
## /var/folders/l_/k6t9zw295sn46sr8s62l6cb80000gn/T//RtmpZ9d9ED/downloaded_packages
install.packages("RColorBrewer")
##
## The downloaded binary packages are in
## /var/folders/l_/k6t9zw295sn46sr8s62l6cb80000gn/T//RtmpZ9d9ED/downloaded_packages
library(arules)
## Warning: package 'arules' was built under R version 3.5.2
## Loading required package: Matrix
## Warning: package 'Matrix' was built under R version 3.5.2
##
## Attaching package: 'arules'
## The following objects are masked from 'package:base':
##
## abbreviate, write
library(arulesViz)
## Warning: package 'arulesViz' was built under R version 3.5.2
## Loading required package: grid
library(RColorBrewer)
data("Groceries")
summary(Groceries)
## transactions as itemMatrix in sparse format with
## 9835 rows (elements/itemsets/transactions) and
## 169 columns (items) and a density of 0.02609146
##
## most frequent items:
## whole milk other vegetables rolls/buns soda
## 2513 1903 1809 1715
## yogurt (Other)
## 1372 34055
##
## element (itemset/transaction) length distribution:
## sizes
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 2159 1643 1299 1005 855 645 545 438 350 246 182 117 78 77 55
## 16 17 18 19 20 21 22 23 24 26 27 28 29 32
## 46 29 14 14 9 11 4 6 1 1 1 1 3 1
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 4.409 6.000 32.000
##
## includes extended item information - examples:
## labels level2 level1
## 1 frankfurter sausage meat and sausage
## 2 sausage sausage meat and sausage
## 3 liver loaf sausage meat and sausage
show the dimensions of the transactions object
print(dim(Groceries))
## [1] 9835 169
print(dim(Groceries)[1]) # 9835 market baskets for shopping trips
## [1] 9835
print(dim(Groceries)[2]) #169 initial store items
## [1] 169
examine frequency for each item with support greater than 0.025
pdf(file="fig_market_basket_initial_item_support.pdf",width = 8.5, height = 11)
itemFrequencyPlot(Groceries, support = 0.025, cex.names=0.8, xlim = c(0,0.3),
type = "relative", horiz = TRUE, col = "dark red", las = 1,
xlab = "Proportion of Market Baskets Containing Item")
dev.off()
## pdf
## 3
explore possibilities for combining similar items
print(head(itemInfo(Groceries)))
## labels level2 level1
## 1 frankfurter sausage meat and sausage
## 2 sausage sausage meat and sausage
## 3 liver loaf sausage meat and sausage
## 4 ham sausage meat and sausage
## 5 meat sausage meat and sausage
## 6 finished products sausage meat and sausage
print(levels(itemInfo(Groceries)[["level1"]])) # 10 levels... too few”
## [1] "canned food" "detergent" "drinks"
## [4] "fresh products" "fruit and vegetables" "meat and sausage"
## [7] "non-food" "perfumery" "processed food"
## [10] "snacks and candies"
print(levels(itemInfo(Groceries)[["level2"]])) # 55 distinct levels
## [1] "baby food" "bags"
## [3] "bakery improver" "bathroom cleaner"
## [5] "beef" "beer"
## [7] "bread and backed goods" "candy"
## [9] "canned fish" "canned fruit/vegetables"
## [11] "cheese" "chewing gum"
## [13] "chocolate" "cleaner"
## [15] "coffee" "condiments"
## [17] "cosmetics" "dairy produce"
## [19] "delicatessen" "dental care"
## [21] "detergent/softener" "eggs"
## [23] "fish" "frozen foods"
## [25] "fruit" "games/books/hobby"
## [27] "garden" "hair care"
## [29] "hard drinks" "health food"
## [31] "jam/sweet spreads" "long-life bakery products"
## [33] "meat spreads" "non-alc. drinks"
## [35] "non-food house keeping products" "non-food kitchen"
## [37] "packaged fruit/vegetables" "perfumery"
## [39] "personal hygiene" "pet food/care"
## [41] "pork" "poultry"
## [43] "pudding powder" "sausage"
## [45] "seasonal products" "shelf-stable dairy"
## [47] "snacks" "soap"
## [49] "soups/sauces" "staple foods"
## [51] "sweetener" "tea/cocoa drinks"
## [53] "vegetables" "vinegar/oils"
## [55] "wine"
aggregate items using the 55 level2 levels for food categories to create a more meaningful set of items
groceries <- aggregate(Groceries, itemInfo(Groceries)[["level2"]])
print(dim(groceries)[1]) # 9835 market baskets for shopping trips
## [1] 9835
print(dim(groceries)[2]) # 55 final store items (categories)
## [1] 55
pdf(file="fig_market_basket_initial_item_support.pdf",width = 8.5, height = 11)
itemFrequencyPlot(groceries, support = 0.025, cex.names=1.0, xlim = c(0,0.5),
type = "relative", horiz = TRUE, col = "blue", las = 1,
xlab = "Proportion of Market Baskets Containing Items")
dev.off()
## pdf
## 3
obtain large set of association rules for items by category and all shoppers this is done by setting very low criteria for support and confidence
first.rules <- apriori(groceries, parameter = list(support = 0.001, confidence = 0.05))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.05 0.1 1 none FALSE TRUE 5 0.001 1
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 9
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[55 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [54 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 5 6 7 8 done [0.01s].
## writing ... [69921 rule(s)] done [0.01s].
## creating S4 object ... done [0.02s].
print(summary(first.rules)) # yields 69,921 rules... too many
## set of 69921 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4 5 6 7 8
## 21 1205 10467 23895 22560 9888 1813 72
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 4.000 4.000 4.502 5.000 8.000
##
## summary of quality measures:
## support confidence lift count
## Min. :0.001017 Min. :0.0500 Min. : 0.4475 Min. : 10.00
## 1st Qu.:0.001118 1st Qu.:0.2110 1st Qu.: 1.8315 1st Qu.: 11.00
## Median :0.001525 Median :0.4231 Median : 2.2573 Median : 15.00
## Mean :0.002488 Mean :0.4364 Mean : 2.5382 Mean : 24.47
## 3rd Qu.:0.002339 3rd Qu.:0.6269 3rd Qu.: 2.9662 3rd Qu.: 23.00
## Max. :0.443010 Max. :1.0000 Max. :16.1760 Max. :4357.00
##
## mining info:
## data ntransactions support confidence
## groceries 9835 0.001 0.05
select association rules using thresholds for support and confidence
second.rules <- apriori(groceries, parameter = list(support = 0.025, confidence = 0.05))
## Apriori
##
## Parameter specification:
## confidence minval smax arem aval originalSupport maxtime support minlen
## 0.05 0.1 1 none FALSE TRUE 5 0.025 1
## maxlen target ext
## 10 rules FALSE
##
## Algorithmic control:
## filter tree heap memopt load sort verbose
## 0.1 TRUE TRUE FALSE TRUE 2 TRUE
##
## Absolute minimum support count: 245
##
## set item appearances ...[0 item(s)] done [0.00s].
## set transactions ...[55 item(s), 9835 transaction(s)] done [0.00s].
## sorting and recoding items ... [32 item(s)] done [0.00s].
## creating transaction tree ... done [0.00s].
## checking subsets of size 1 2 3 4 done [0.00s].
## writing ... [344 rule(s)] done [0.00s].
## creating S4 object ... done [0.00s].
print(summary(second.rules)) # yields 344 rules
## set of 344 rules
##
## rule length distribution (lhs + rhs):sizes
## 1 2 3 4
## 21 162 129 32
##
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 2.0 2.0 2.5 3.0 4.0
##
## summary of quality measures:
## support confidence lift count
## Min. :0.02542 Min. :0.05043 Min. :0.6669 Min. : 250.0
## 1st Qu.:0.03030 1st Qu.:0.18202 1st Qu.:1.2498 1st Qu.: 298.0
## Median :0.03854 Median :0.39522 Median :1.4770 Median : 379.0
## Mean :0.05276 Mean :0.37658 Mean :1.4831 Mean : 518.9
## 3rd Qu.:0.05236 3rd Qu.:0.51271 3rd Qu.:1.7094 3rd Qu.: 515.0
## Max. :0.44301 Max. :0.79841 Max. :2.4073 Max. :4357.0
##
## mining info:
## data ntransactions support confidence
## groceries 9835 0.025 0.05
data visualization of association rules in scatter plot
plot(second.rules, control=list(jitter=2, col = rev(brewer.pal(9, "Greens")[4:9])), shading = "lift") # not working
itemFrequencyPlot(Groceries,topN=20,type="absolute",col=brewer.pal(8,'Pastel2'), main="Absolute Item Frequency Plot")
plotly_arules(second.rules)
## Warning: 'plotly_arules' is deprecated.
## Use 'plot' instead.
## See help("Deprecated")
## To reduce overplotting, jitter is added! Use jitter = 0 to prevent jitter.
plot(second.rules, method = "graph", engine = "htmlwidget")
## Warning: Too many rules supplied. Only plotting the best 100 rules using
## lift (change control parameter max if needed)
second.rules2<-head(second.rules, n=20, by="lift")
plot(second.rules2, method="paracoord")
